System, method, and computer program product for generating code to retrieve aggregation data for machine learning models

ABSTRACT

Provided is a system that includes at least one processor programmed or configured to receive an XML data file, wherein the XML data file includes data associated with one or more input parameters of a machine learning model, generate a code generation template based on the data associated with one or more input parameters of the machine learning model included in the XML file, where the code generation template includes one or more keys associated with one or more parameters of a transaction aggregate for an account of a user, and generate a file of executable code based on the code generation template, wherein the file of executable code includes instructions that, when executed by at least one processor, causes at least one processor to retrieve transaction aggregate data associated with the transaction aggregate for the account of the user. A method and computer program product are also provided.

CROSS-REFERENCE TO RELATED APPLICATION

This application is the United States national phase of InternationalApplication No. PCT/US2019/049790 filed Sep. 5, 2019, the entiredisclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND 1. Field

This disclosure relates generally to machine learning models and, insome non-limiting aspects or embodiments, to systems, methods, andcomputer program products for generating transaction aggregationsassociated with predictions for transactions that may be used with amachine learning model.

2. Technical Considerations

Machine learning may be a field of computer science that usesstatistical techniques to provide a computer system with the ability tolearn (e.g., to progressively improve performance of) a task with datawithout the computer system being explicitly programmed to perform thetask. In some instances, a machine learning model may be developed for aset of data so that the machine learning model may perform a task (e.g.,a task associated with a prediction) with regard to the set of data.

In some instances, a machine learning model, such as a predictivemachine learning model, may be used to make a prediction associated witha risk or an opportunity. A predictive machine learning model may beused to analyze a relationship between the performance of a unit basedon data associated with the unit and one or more known features of theunit.

In some examples, a predictive machine learning model may require aninput that includes features that are based on an aggregation of data(e.g., aggregation data). For example, the predictive machine learningmodel may require an input that includes features that are based onaggregation data that is calculated in real-time. However, calculatingthe aggregation data in real-time may be very resource intensive and mayrequire a large amount of time to calculate.

Further, a machine learning model developed for one set of data may notbe applicable to a certain problem in another set of data. For example,a machine learning model developed for a first data set associated witha particular geographic area and/or demographic may not be applicable toa certain prediction in a second set of data associated with a differentgeographic area and/or demographic. This may lead to the creation of alarge number of machine learning models developed for each set of dataand also a large amount of data for each machine learning model.

SUMMARY

Accordingly, disclosed are systems, methods, and computer programproducts for generating code to retrieve aggregation data for machinelearning models.

According to some non-limiting aspects or embodiments, provided is asystem, comprising: at least one processor programmed or configured to:receive an Extensible Markup Language (XML) data file, wherein the XMLdata file comprises data associated with one or more input parameters ofa machine learning model; generate a code generation template based onthe data associated with one or more input parameters of the machinelearning model included in the XML file, where the code generationtemplate comprises one or more keys associated with one or moreparameters of a transaction aggregate for an account of a user, whereinthe one or more parameters of the transaction aggregate for the accountof the user are based on one or more parameters of a plurality ofpayment transactions involving the account of the user; and generate afile of executable code based on the code generation template, whereinthe file of executable code comprises instructions that, when executedby at least one processor, causes at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user.

According to some non-limiting aspects or embodiments, provided is acomputer-implemented method, comprising: receiving, with at least oneprocessor, a data file, wherein the data file comprises data associatedwith one or more input parameters of a machine learning model;generating, with at least one processor, a code generation templatebased on the data associated with one or more input parameters of themachine learning model included in the data file, where the codegeneration template comprises one or more keys associated with one ormore parameters of a transaction aggregate for an account of a user;generating, with at least one processor, an executable code file basedon the code generation template, wherein the file of executable codecomprises instructions that, when executed by at least one processor,cause the at least one processor to retrieve transaction aggregate dataassociated with the transaction aggregate for the account of the user.

According to some non-limiting aspects or embodiments, provided is acomputer program product, comprising: at least one non-transitorycomputer-readable medium comprising one or more instructions that, whenexecuted by at least one processor, cause the at least one processor to:receive a data file associated with a machine learning model; generate acode generation template based on receiving the data file associatedwith the machine learning model, where the code generation templatecomprises one or more keys associated with one or more parameters of atransaction aggregate for an account of a user, wherein the one or moreparameters of the transaction aggregate for the account of the user arebased on one or more parameters of a plurality of payment transactionsinvolving the account of the user; and generate a file of executablecode based on the code generation template, wherein the file ofexecutable code comprises instructions that, when executed by the atleast one processor, cause the at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user.

Further non-limiting aspects or embodiments are set forth in thefollowing numbered clauses:

Clause 1: A system, comprising: at least one processor programmed orconfigured to: receive an XML data file, wherein the XML data filecomprises data associated with one or more input parameters of a machinelearning model; generate a code generation template based on the dataassociated with one or more input parameters of the machine learningmodel included in the XML file, where the code generation templatecomprises one or more keys associated with one or more parameters of atransaction aggregate for an account of a user, wherein the one or moreparameters of the transaction aggregate for the account of the user arebased on one or more parameters of a plurality of payment transactionsinvolving the account of the user; and generate a file of executablecode based on the code generation template, wherein the file ofexecutable code comprises instructions that, when executed by at leastone processor, cause at least one processor to retrieve transactionaggregate data associated with the transaction aggregate for the accountof the user.

Clause 2: The system of clause 1, wherein the code generation templatecomprises a template for an SQL query and wherein the template for theSQL query comprises the one or more keys associated with one or moreparameters of the transaction aggregate for the account of the user.

Clause 3: The system of clauses 1 or 2, wherein, when generating thefile of executable code, the at least one processor is programmed orconfigured to: generate the file of executable code that, when executedby at least one processor, causes at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user from a predetermined database of a plurality ofdatabases.

Clause 4: The system of any of clauses 1-3, wherein the at least oneprocessor is further programmed or configured to: receive a request forrisk assessment determination of a payment transaction involving theaccount of the user from a client device; retrieve first transactionaggregate data associated with a first transaction aggregate for theaccount of the user; and determine a risk assessment score associatedwith the payment transaction involving the account of the user using thefirst transaction aggregate data associated with the first transactionaggregate for the account of the user as an input to the machinelearning model.

Clause 5: The system of any of clauses 1-4, wherein, when retrievingfirst transaction aggregate data associated with the first transactionaggregate for the account of the user, the at least one processor isprogrammed or configured to: retrieve short-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a short-term database; and retrieve long-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a long-term database.

Clause 6: The system of any of clauses 1-5, wherein, when retrieving thefirst transaction aggregate data associated with the first transactionaggregate for the account of the user, the at least one processor isprogrammed or configured to: retrieve the first transaction aggregatedata associated with the first transaction aggregate for the account ofthe user from one or more databases based on executing the file ofexecutable code.

Clause 7: The system of any of clauses 1-6, wherein, when receiving theXML file, the at least one processor is programmed or configured to:receive the XML file from a client device via a web application, andwherein the data associated with one or more input parameters of themachine learning model is based on data received via the webapplication.

Clause 8: A computer-implemented method, comprising: receiving, with atleast one processor, a data file, wherein the data file comprises dataassociated with one or more input parameters of a machine learningmodel; generating, with at least one processor, a code generationtemplate based on the data associated with one or more input parametersof the machine learning model included in the data file, where the codegeneration template comprises one or more keys associated with one ormore parameters of a transaction aggregate for an account of a user; andgenerating, with at least one processor, an executable code file basedon the code generation template, wherein the file of executable codecomprises instructions that, when executed by at least one processor,cause the at least one processor to retrieve transaction aggregate dataassociated with the transaction aggregate for the account of the user.

Clause 9: The method of clause 8, wherein generating the file ofexecutable code file comprises: generating the file of executable codethat, when executed by at least one processor, causes at least oneprocessor to retrieve transaction aggregate data associated with thetransaction aggregate for the account of the user from a predetermineddatabase of a plurality of databases.

Clause 10: The method of clauses 8 or 9, further comprising: receiving arequest for risk assessment determination of a payment transactioninvolving the account of the user from a client device; retrieving firsttransaction aggregate data associated with a first transaction aggregatefor the account of the user; and determining a risk assessment scoreassociated with the payment transaction involving the account of theuser using the first transaction aggregate data associated with thefirst transaction aggregate for the account of the user as an input tothe machine learning model.

Clause 11: The method of any of clauses 8-10, wherein retrieving firsttransaction aggregate data associated with the first transactionaggregate for the account of the user comprises: retrieving short-termaggregation data associated with the first transaction aggregate for theaccount of the user from a short-term database; and retrieving long-termaggregation data associated with the first transaction aggregate for theaccount of the user from a long-term database.

Clause 12: The method of any of clauses 8-11, wherein retrieving thefirst transaction aggregate data associated with the first transactionaggregate for the account of the user, the at least one processor isprogrammed or configured to: retrieve the first transaction aggregatedata associated with the first transaction aggregate for the account ofthe user from one or more databases based on executing the file ofexecutable code.

Clause 13: The method of any of clauses 8-12, further comprising:determining whether the risk assessment score associated with thepayment transaction involving the account of the user satisfies a riskassessment threshold; and performing an action based on determining thatthe risk assessment score associated with the payment transactioninvolving the account of the user satisfies a risk assessment threshold.

Clause 14: The method of any of clauses 8-13, wherein receiving the datafile comprises: receiving an XML file from a client device via a webapplication, and wherein the data associated with one or more inputparameters of the machine learning model is based on data received viathe web application.

Clause 15: A computer program product comprising at least onenon-transitory computer-readable medium comprising one or moreinstructions that, when executed by at least one processor, cause the atleast one processor to: receive a data file associated with a machinelearning model; generate a code generation template based on receivingthe data file associated with the machine learning model, where the codegeneration template comprises one or more keys associated with one ormore parameters of a transaction aggregate for an account of a user,wherein the one or more parameters of the transaction aggregate for theaccount of the user are based on one or more parameters of a pluralityof payment transactions involving the account of the user; and generatea file of executable code based on the code generation template, whereinthe file of executable code comprises instructions that, when executedby the at least one processor, cause the at least one processor toretrieve transaction aggregate data associated with the transactionaggregate for the account of the user.

Clause 16: The computer program product of clause 15, wherein the codegeneration template comprises a template for an SQL query and whereinthe template for the SQL query comprises the one or more keys associatedwith one or more parameters of the transaction aggregate for the accountof the user.

Clause 17: The computer program product of clauses 15 or 16, wherein,the one or more instructions that cause the at least one processor togenerate the file of executable code file, cause the at least oneprocessor to: generate the file of executable code that, when executedby at least one processor, causes at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user from a predetermined database of a plurality ofdatabases.

Clause 18: The computer program product of any of clauses 15-17, whereinthe one or more instructions further cause the at least one processorto: receive a request for risk assessment determination of a paymenttransaction involving the account of the user from a client device;retrieve first transaction aggregate data associated with a firsttransaction aggregate for the account of the user; and determine a riskassessment score associated with the payment transaction involving theaccount of the user using the first transaction aggregate dataassociated with the first transaction aggregate for the account of theuser as an input to the machine learning model.

Clause 19: The computer program product of any of clauses 15-18,wherein, the one or more instructions that cause the at least oneprocessor to retrieve the first transaction aggregate data associatedwith the first transaction aggregate for the account of the user, causethe at least one processor to: retrieve short-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a short-term database; and retrieve long-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a long-term database.

Clause 20: The computer program product of any of clauses 15-19,wherein, the one or more instructions that cause the at least oneprocessor to receive the XML file, cause the at least one processor to:receive the XML file from a client device via a web application, andwherein the data associated with one or more input parameters of themachine learning model is based on data received via the webapplication.

These and other features and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the presentdisclosure. As used in the specification and the claims, the singularform of “a,” “an,” and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details of non-limiting aspects or embodimentsare explained in greater detail below with reference to the exemplaryembodiments that are illustrated in the accompanying schematic figures,in which:

FIG. 1 is a diagram of a non-limiting aspect or embodiment of anenvironment in which devices, systems, methods, and/or productsdescribed herein may be implemented;

FIG. 2 is a diagram of a non-limiting aspect or embodiment of componentsof one or more devices and/or one or more systems of FIG. 1;

FIG. 3 is a flowchart of a non-limiting aspect or embodiment of aprocess for generating code to retrieve aggregation data for machinelearning models; and

FIGS. 4A-4E are diagrams of a non-limiting embodiment of animplementation of a process for generating code to retrieve aggregationdata for machine learning models.

DESCRIPTION

For purposes of the description hereinafter, the terms “end,” “upper,”“lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,”“lateral,” “longitudinal,” and derivatives thereof shall relate to thedisclosure as it is oriented in the drawing figures. However, it is tobe understood that the disclosure may assume various alternativevariations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification, are simply exemplary embodiments or aspects ofthe disclosure. Hence, specific dimensions and other physicalcharacteristics related to the embodiments or aspects of the embodimentsdisclosed herein are not to be considered as limiting unless otherwiseindicated.

No aspect, component, element, structure, act, step, function,instruction, and/or the like used herein should be construed as criticalor essential unless explicitly described as such. Also, as used herein,the articles “a” and “an” are intended to include one or more items, andmay be used interchangeably with “one or more” and “at least one.”Furthermore, as used herein, the term “set” is intended to include oneor more items (e.g., related items, unrelated items, a combination ofrelated and unrelated items, and/or the like) and may be usedinterchangeably with “one or more” or “at least one.” Where only oneitem is intended, the term “one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms. Further, the phrase “based on” is intended tomean “based at least partially on” unless explicitly stated otherwise.

As used herein, the terms “communication” and “communicate” may refer tothe reception, receipt, transmission, transfer, provision, and/or thelike of information (e.g., data, signals, messages, instructions,commands, and/or the like). For one unit (e.g., a device, a system, acomponent of a device or system, combinations thereof, and/or the like)to be in communication with another unit means that the one unit is ableto directly or indirectly receive information from and/or send (e.g.,transmit) information to the other unit. This may refer to a direct orindirect connection that is wired and/or wireless in nature.Additionally, two units may be in communication with each other eventhough the information transmitted may be modified, processed, relayed,and/or routed between the first and second unit. For example, a firstunit may be in communication with a second unit even though the firstunit passively receives information and does not actively sendinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and sends theprocessed information to the second unit. In some non-limitingembodiments, a message may refer to a network packet (e.g., a datapacket and/or the like) that includes data.

As used herein, the terms “issuer,” “issuer institution,” “issuer bank,”or “payment device issuer,” may refer to one or more entities thatprovide accounts to individuals (e.g., users, customers, and/or thelike) for conducting payment transactions such as such as credit paymenttransactions and/or debit payment transactions. For example, an issuerinstitution may provide an account identifier, such as a primary accountnumber (PAN), to a customer that uniquely identifies one or moreaccounts associated with that customer. In some non-limitingembodiments, an issuer may be associated with a bank identificationnumber (BIN) that uniquely identifies the issuer institution. As usedherein, the term “issuer system” may refer to one or more computersystems operated by or on behalf of an issuer, such as a serverexecuting one or more software applications. For example, an issuersystem may include one or more authorization servers for authorizing atransaction.

As used herein, the term “account identifier” may include one or moretypes of identifiers associated with an account (e.g., a PAN associatedwith an account, a card number associated with an account, a paymentcard number associated with an account, a token associated with anaccount, and/or the like). In some non-limiting embodiments, an issuermay provide an account identifier (e.g., a PAN, a token, and/or thelike) to a user (e.g., an account holder) that uniquely identifies oneor more accounts associated with that user. The account identifier maybe embodied on a payment device (e.g., a physical instrument used forconducting payment transactions, such as a payment card, a credit card,a debit card, a gift card, and/or the like) and/or may be electronicinformation communicated to the user that the user may use forelectronic payment transactions. In some non-limiting embodiments, theaccount identifier may be an original account identifier, where theoriginal account identifier was provided to a user at the creation ofthe account associated with the account identifier. In some non-limitingembodiments, the account identifier may be a supplemental accountidentifier, which may include an account identifier that is provided toa user after the original account identifier was provided to the user.For example, if the original account identifier is forgotten, stolen,and/or the like, a supplemental account identifier may be provided tothe user. In some non-limiting embodiments, an account identifier may bedirectly or indirectly associated with an issuer institution such thatan account identifier may be a token that maps to a PAN or other type ofaccount identifier. Account identifiers may be alphanumeric, anycombination of characters and/or symbols, and/or the like.

As used herein, the term “token” may refer to an account identifier ofan account that is used as a substitute or replacement for anotheraccount identifier, such as a PAN. Tokens may be associated with a PANor other original account identifier in one or more data structures(e.g., one or more databases) such that they may be used to conduct apayment transaction without directly using an original accountidentifier. In some non-limiting embodiments, an original accountidentifier, such as a PAN, may be associated with a plurality of tokensfor different individuals or purposes. In some non-limiting embodiments,tokens may be associated with a PAN or other account identifiers in oneor more data structures such that they can be used to conduct atransaction without directly using the PAN or the other accountidentifiers. In some examples, an account identifier, such as a PAN, maybe associated with a plurality of tokens for different uses or differentpurposes.

As used herein, the term “merchant” may refer to one or more entities(e.g., operators of retail businesses) that provide goods, services,and/or access to goods and/or services, to a user (e.g., a customer, aconsumer, and/or the like) based on a transaction such as a paymenttransaction. As used herein, the term “merchant system” may refer to oneor more computer systems operated by or on behalf of a merchant, such asa server executing one or more software applications. As used herein,the term “product” may refer to one or more goods and/or servicesoffered by a merchant.

As used herein, the term “point-of-sale (POS) device” may refer to oneor more electronic devices, which may be used by a merchant to conduct atransaction (e.g., a payment transaction) and/or process a transaction.Additionally or alternatively, a POS device may include peripheraldevices, card readers, scanning devices (e.g., code scanners and/or thelike), Bluetooth® communication receivers, near-field communication(NFC) receivers, radio frequency identification (RFID) receivers, and/orother contactless transceivers or receivers, contact-based receivers,payment terminals, and/or the like.

As used herein, the term “point-of-sale (POS) system” may refer to oneor more client devices and/or peripheral devices used by a merchant toconduct a transaction. For example, a POS system may include one or morePOS devices and/or other like devices that may be used to conduct apayment transaction. In some non-limiting embodiments, a POS system(e.g., a merchant POS system) may include one or more server computersprogrammed or configured to process online payment transactions throughwebpages, mobile applications, and/or the like.

As used herein, the term “transaction service provider” may refer to anentity that receives transaction authorization requests from merchantsor other entities and provides guarantees of payment, in some casesthrough an agreement between the transaction service provider and anissuer institution. In some non-limiting embodiments, a transactionservice provider may include a credit card company, a debit cardcompany, a payment network such as Visa®, MasterCard®, AmericanExpress®, or any other entity that processes transaction. As usedherein, the term “transaction service provider system” may refer to oneor more computer systems operated by or on behalf of a transactionservice provider, such as a transaction service provider systemexecuting one or more software applications. A transaction serviceprovider system may include one or more processors and, in somenon-limiting embodiments, may be operated by or on behalf of atransaction service provider.

As used herein, the term “payment device” may refer to a payment card(e.g., a credit or debit card), a gift card, a smart card (e.g., a chipcard, an integrated circuit card, and/or the like), smart media, apayroll card, a healthcare card, a wristband, a machine-readable mediumcontaining account information, a keychain device or fob, an RFIDtransponder, a retailer discount or loyalty card, and/or the like. Thepayment device may include a volatile or a non-volatile memory to storeinformation (e.g., an account identifier, a name of the account holder,and/or the like).

As used herein, the terms “client” and “client device” may refer to oneor more computing devices, such as processors, storage devices, and/orsimilar computer components, that access a service made available by aserver. In some non-limiting embodiments, a “client device” may refer toone or more devices that facilitate payment transactions, such as one ormore POS devices used by a merchant. In some non-limiting embodiments, aclient device may include a computing device configured to communicatewith one or more networks and/or facilitate payment transactions suchas, but not limited to, one or more desktop computers, one or moremobile devices, and/or other like devices. Moreover, a “client” may alsorefer to an entity, such as a merchant, that owns, utilizes, and/oroperates a client device for facilitating payment transactions with atransaction service provider.

As used herein, the term “server” may refer to one or more computingdevices, such as processors, storage devices, and/or similar computercomponents that communicate with client devices and/or other computingdevices over a network, such as the Internet or private networks and, insome examples, facilitate communication among other servers and/orclients.

As used herein, the term “system” may refer to one or more computingdevices or combinations of computing devices such as, but not limitedto, processors, servers, client devices, software applications, and/orother like components. In addition, reference to “a server” or “aprocessor,” as used herein, may refer to a previously-recited serverand/or processor that is recited as performing a previous step orfunction, a different server and/or processor, and/or a combination ofservers and/or processors. For example, as used in the specification andthe claims, a first server and/or a first processor that is recited asperforming a first step or function may refer to the same or differentserver and/or a processor recited as performing a second step orfunction.

In some non-limiting embodiments, systems, computer-implemented methods,and computer program products are disclosed. For example, a system mayinclude at least one processor programmed or configured to receive adata file associated with machine learning model, such as an XML datafile. The data file may include data associated with one or more inputparameters of a machine learning model. The at least one processor maybe programmed or configured to generate a code generation template basedon the data associated with one or more input parameters of the machinelearning model included in the data file. The code generation templatemay include one or more keys associated with one or more parameters of atransaction aggregate for an account of a user and the one or moreparameters of the transaction aggregate for the account of the user maybe based on one or more parameters of a plurality of paymenttransactions involving the account of the user. The at least oneprocessor may be programmed or configured to generate a file ofexecutable code based on the code generation template. The file ofexecutable code may include instructions that, when executed by at leastone processor, cause at least one processor to retrieve transactionaggregate data associated with the transaction aggregate for the accountof the user.

In this way, non-limiting embodiments of the present disclosure mayallow for aggregation data to be determined that is less resourceintensive and that does not require a lot of time to calculate, forexample, once a request associated with the aggregation data isreceived. Further, non-limiting embodiments of the present disclosuremay allow for the machine learning models that are created for aspecific set of aggregation data and that do not require aggregationdata to be recalculated during every instance of use of the machinelearning model.

Referring now to FIG. 1, FIG. 1 is a diagram of an example environment100 in which devices, systems, methods, and/or products described hereinmay be implemented. As shown in FIG. 1, environment 100 includes codegenerator system 102, user device 104, orchestration system 106,artificial intelligence (AI) risk determination system 108, short-termdatabase 110, long-term database 112, and communication network 114.Code generator system 102, user device 104, orchestration system 106, AIrisk determination system 108, short-term database 110, and long-termdatabase 112 may interconnect (e.g., establish a connection tocommunicate, and/or the like) via wired connections, wirelessconnections, or a combination of wired and wireless connections.

Code generator system 102 may include one or more devices capable ofbeing in communication with user device 104, orchestration system 106,artificial intelligence (AI) risk determination system 108, short-termdatabase 110, and long-term database 112 via communication network 114.For example, code generator system 102 may include one or more computingdevices, such as one or more servers and/or other like devices. In somenon-limiting embodiments, code generator system 102 may be associatedwith a transaction service provider, as described herein. For example,code generator system 102 may be operated by a transaction serviceprovider and/or code generator system 102 may be a component of atransaction service provider system.

User device 104 may include one or more devices capable of being incommunication with code generator system 102, orchestration system 106,AI risk determination system 108, short-term database 110, and long-termdatabase 112 via communication network 114. For example, user device 104may include one or more computing devices, such as one or more servers,one or more client devices, one or more mobile devices, and/or otherlike devices. In some non-limiting embodiments, user device 104 may beassociated with a transaction service provider, as described herein. Forexample, user device 104 may be operated by a transaction serviceprovider and/or user device 104 may be a component of a transactionservice provider system.

Orchestration system 106 may include one or more devices capable ofbeing in communication with code generator system 102, user device 104,AI risk determination system 108, short-term database 110, and long-termdatabase 112 via communication network 114. For example, orchestrationsystem 106 may include one or more computing devices, such as one ormore servers and/or other like devices. In some non-limitingembodiments, orchestration system 106 may be associated with atransaction service provider, as described herein. For example,orchestration system 106 may be operated by a transaction serviceprovider and/or orchestration system 106 may be a component of atransaction service provider system.

AI risk determination system 108 may include one or more devices capableof being in communication with code generator system 102, user device104, orchestration system 106, short-term database 110, and long-termdatabase 112 via communication network 114. For example, AI riskdetermination system 108 may include one or more computing devices, suchone or more servers and/or other like devices. In some non-limitingembodiments, AI risk determination system 108 may be associated with atransaction service provider, as described herein. For example, AI riskdetermination system 108 may be operated by a transaction serviceprovider and/or AI risk determination system 108 may be a component of atransaction service provider system.

Short-term database 110 may include one or more devices capable ofstoring data in a data structure and capable of being in communicationwith code generator system 102, user device 104, orchestration system106, AI risk determination system 108, and long-term database 112 viacommunication network 114. For example, short-term database 110 mayinclude one or more computing devices, such as one or more serversand/or other like devices. In some non-limiting embodiments, short-termdatabase 110 may be a component of a system. For example, short-termdatabase 110 may be a component of code generator system 102 and/ororchestration system 106. In some non-limiting embodiments, short-termdatabase 110 may receive and/or store data associated with real-time(e.g., live) payment transactions. For example, short-term database 110may receive the data associated with real-time payment transactions froma messaging system, such as Apache Kafka or IBM MQ, and short-termdatabase 110 may store the data associated with real-time paymenttransactions in a data structure based on receiving the data from themessaging system.

Long-term database 112 may include one or more devices capable ofstoring data in a data structure and capable of being in communicationwith code generator system 102, user device 104, orchestration system106, AI risk determination system 108, and short-term database 110 viacommunication network 114. For example, long-term database 112 mayinclude one or more computing devices, such as one or more serversand/or other like devices. In some non-limiting embodiments, long-termdatabase 112 may be a component of a system. For example, long-termdatabase 112 may be a component of code generator system 102 and/ororchestration system 106. In some non-limiting embodiments, long-termdatabase 112 may receive and/or store data associated with user profilesbased on transaction data associated with payment transactions conductedby users associated with the user profiles. For example, long-termdatabase 112 may receive the data associated with user profiles from afile system, such as an Apache Hadoop system, and long-term database 112may store the data associated with user profiles based on receiving thedata from the file system.

Communication network 114 may include one or more wired and/or wirelessnetworks. For example, communication network 114 may include a cellularnetwork (e.g., a long-term evolution (LTE) network, a third generation(3G) network, a fourth generation (4G) network, a fifth generation (5G)network, a code division multiple access (CDMA) network, and/or thelike), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the public switched telephone network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, a cloud computing network, and/or the like, and/ora combination of some or all of these or other types of networks.

The number and arrangement of systems and/or devices shown in FIG. 1 areprovided as an example. There may be additional systems and/or devices,fewer systems and/or devices, different systems and/or devices, ordifferently arranged systems and/or devices than those shown in FIG. 1.Furthermore, two or more systems and/or devices shown in FIG. 1 may beimplemented within a single system or a single device, or a singlesystem or a single device shown in FIG. 1 may be implemented asmultiple, distributed systems or devices. Additionally or alternatively,a set of systems or a set of devices (e.g., one or more systems, one ormore devices) of environment 100 may perform one or more functionsdescribed as being performed by another set of systems or another set ofdevices of environment 100.

Referring now to FIG. 2, FIG. 2 is a diagram of example components ofdevice 200. Device 200 may correspond to code generator system 102(e.g., one or more devices of code generator system 102), user device104, orchestration system 106 (e.g., one or more devices oforchestration system 106), AI risk determination system 108 (e.g., oneor more devices of AI risk determination system 108), short-termdatabase 110, and/or long-term database 112. In some non-limitingaspects or embodiments, code generator system 102, user device 104,orchestration system 106, AI risk determination system 108, short-termdatabase 110, and/or long-term database 112 may include at least onedevice 200 and/or at least one component of device 200. As shown in FIG.2, device 200 may include bus 202, processor 204, memory 206, storagecomponent 208, input component 210, output component 212, andcommunication interface 214.

Bus 202 may include a component that permits communication among thecomponents of device 200. In some non-limiting aspects or embodiments,processor 204 may be implemented in hardware, software, or a combinationof hardware and software. For example, processor 204 may include aprocessor (e.g., a central processing unit (CPU), a graphics processingunit (GPU), an accelerated processing unit (APU), and/or the like), amicroprocessor, a digital signal processor (DSP), and/or any processingcomponent (e.g., a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), and/or the like) thatcan be programmed to perform a function. Memory 206 may include randomaccess memory (RAM), read-only memory (ROM), and/or another type ofdynamic or static storage device (e.g., flash memory, magnetic memory,optical memory, and/or the like) that stores information and/orinstructions for use by processor 204.

Storage component 208 may store information and/or software related tothe operation and use of device 200. For example, storage component 208may include a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, and/or the like), a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of computer-readable medium, alongwith a corresponding drive.

Input component 210 may include a component that permits device 200 toreceive information, such as via user input (e.g., a touchscreendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, a camera, and/or the like). Additionally or alternatively,input component 210 may include a sensor for sensing information (e.g.,a global positioning system (GPS) component, an accelerometer, agyroscope, an actuator, and/or the like). Output component 212 mayinclude a component that provides output information from device 200(e.g., a display, a speaker, one or more light-emitting diodes (LEDs),and/or the like).

Communication interface 214 may include a transceiver-like component(e.g., a transceiver, a separate receiver and transmitter, and/or thelike) that enables device 200 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 214 may permit device200 to receive information from another device and/or provideinformation to another device. For example, communication interface 214may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a WiFi® interface, a cellularnetwork interface, and/or the like.

Device 200 may perform one or more processes described herein. Device200 may perform these processes based on processor 204 executingsoftware instructions stored by a computer-readable medium, such asmemory 206 and/or storage component 208. A computer-readable medium(e.g., a non-transitory computer-readable medium) is defined herein as anon-transitory memory device. A non-transitory memory device includesmemory space located inside of a single physical storage device ormemory space spread across multiple physical storage devices.

Software instructions may be read into memory 206 and/or storagecomponent 208 from another computer-readable medium or from anotherdevice via communication interface 214. When executed, softwareinstructions stored in memory 206 and/or storage component 208 may causeprocessor 204 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments or aspects describedherein are not limited to any specific combination of hardware circuitryand software.

Memory 206 and/or storage component 208 may include data storage or oneor more data structures (e.g., a database, and/or the like). Device 200may be capable of retrieving information from, storing information in,or searching information stored in the data storage or one or more datastructures in memory 206 and/or storage component 208. For example, theinformation may include encryption data, input data, output data,transaction data, account data, or any combination thereof.

The number and arrangement of components shown in FIG. 2 are provided asan example. In some non-limiting aspects or embodiments, device 200 mayinclude additional components, fewer components, different components,or differently arranged components than those shown in FIG. 2.Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 200 may perform one or more functions described asbeing performed by another set of components of device 200.

Referring now to FIG. 3, FIG. 3 is a flowchart of a non-limitingembodiment of a process 300 for generating code to retrieve aggregationdata for machine learning models. In some non-limiting aspects orembodiments, one or more of the functions described with respect toprocess 300 may be performed (e.g., completely, partially, and/or thelike) by code generator system 102. In some non-limiting embodiments,one or more of the steps of process 300 may be performed (e.g.,completely, partially, and/or the like) by another device or a group ofdevices separate from and/or including code generator system 102 suchas, for example, user device 104, orchestration system 106, and/or AIrisk determination system 108.

As shown in FIG. 3, at step 302, process 300 may include receiving adata file associated with a machine learning model. For example, codegenerator system 102 may receive the data file that includes one or moreinput parameters of a machine learning model. In some non-limitingembodiments, the one or more input parameters of the machine learningmodel may include one or more features associated with the machinelearning model. For example, the one or more input parameters of themachine learning model may correspond to one or more features that wereused to train and/or validate the machine learning model.

In some non-limiting embodiments, the one or more input parameters ofthe machine learning model may include one or more keys associated withone or more parameters of a transaction aggregate for an account of auser. The one or more keys associated with the one or more parameters ofthe transaction aggregate for the account of the user may correspond toone or more features that were used to train and/or validate the machinelearning model. In some non-limiting embodiments, the machine learningmodel may include a risk determination machine model. For example, themachine learning model may include a risk determination machine modelthat is used by AI risk determination system 108 to determine a riskassociated with authorization of a payment transaction conducted (e.g.,a payment transaction conducted in real-time) with the account of theuser.

In some non-limiting embodiments, the one or more parameters of thetransaction aggregate for the account of the user may be based on one ormore parameters of a plurality of payment transactions involving theaccount of the user. For example, the one or more parameters of thetransaction aggregate may include a number of payment transactionsconducted with the account of the user during a time interval, a totaltransaction amount of a plurality of payment transactions conducted withthe account of the user during a time interval, a number of IP addresses(e.g., a number of IP addresses each associated with a differentcomputing device) involved in a plurality of payment transactionsconducted with the account of the user during a time interval, a list ofIP addresses associated with (e.g., a list of IP addresses eachassociated with a different computing device involved in) a plurality ofpayment transactions conducted with the account of the user during atime interval, and/or the like.

In some non-limiting embodiments, code generator system 102 may storethe one or more parameters of the transaction aggregate for the accountof the user. For example, code generator system 102 may store the one ormore parameters of the transaction aggregate for the account of the useras a user profile for the account of the user in long-term database 112.The one or more parameters of the transaction aggregate for the accountof the user (e.g., the user profile for the account of the user) may beassigned to an account identifier of the account of the user inlong-term database 112.

In some non-limiting embodiments, code generator system 102 may receivethe data file from user device 104 via a web application. For example,code generator system 102 may receive the data file from user device 104via the web application that is provided on user device 104. The dataincluded in the data file (e.g., data associated with one or more inputparameters of the machine learning model) may be based on data receivedvia the web application. In some non-limiting embodiments, the data filemay include an Extensible Markup Language (XML) data file. For example,the data file may include data in an XML data file format.

As shown in FIG. 3, at step 304, process 300 may include generating acode generation template based on the data file. For example, codegenerator system 102 may generate the code generation template based onthe data file received from user device 104. In some non-limitingembodiments, the code generation template may include a template withfields based on the one or more input parameters of a machine learningmodel (e.g., a risk determination machine learning model). In somenon-limiting embodiments, code generator system 102 may generate thecode generation template based on the machine learning model. Forexample, code generator system 102 may generate the code generationtemplate based on an identifier of the machine learning model, such asan identifier of a risk determination machine learning model. In somenon-limiting embodiments, the identifier of the machine learning modelmay be included in the data file received from user device 104.

In some non-limiting embodiments, code generator system 102 may generatethe code generation template as a template for an SQL query. Forexample, code generator system 102 may generate the code generationtemplate as a template for the SQL query based on the data included inthe data file received from user device 104. In some non-limitingembodiments, the template for the SQL query may include one or more keysassociated with one or more parameters of a transaction aggregate forthe account of the user.

As shown in FIG. 3, at step 306, process 300 may include generating afile of executable code based on the code generation template. Forexample, code generator 102 may generate the file of executable codebased on the code generation template. The file of executable code mayinclude instructions that, when executed, cause transaction aggregatedata associated with a transaction aggregate for an account of a user tobe retrieved. In some non-limiting embodiments, transaction aggregatedata associated with a transaction aggregate for an account of a usermay include one of more values of one or more parameters of thetransaction aggregate for the account of the user. For example, thetransaction aggregate data associated with the transaction aggregate forthe account of the user may include a number of payment transactionsconducted with the account of the user during a time interval where thenumber is equal to 7. In another example, the transaction aggregate dataassociated with the transaction aggregate for the account of the usermay include a total transaction amount of a plurality of paymenttransactions conducted with the account of the user during a timeinterval where the total transaction amount of the plurality of paymenttransactions is equal to $534.56. In another example, the transactionaggregate data associated with the transaction aggregate for the accountof the user may include a number of IP addresses involved in a pluralityof payment transactions conducted with the account of the user during atime interval and the number of IP addresses is equal to 6.

In some non-limiting embodiments, code generator system 102 may storetransaction aggregate data associated with a transaction aggregate forthe account of the user (e.g., one of more values of the one or moreparameters of the transaction aggregate for the account of the user).For example, code generator system 102 may store one or more values ofthe one or more parameters of the transaction aggregate for the accountof the user in a user profile for the account of the user in long-termdatabase 112. The one or more values of the one or more parameters ofthe transaction aggregate for the account of the user (e.g., the userprofile for the account of the user) may be assigned to the accountidentifier of the account of the user in long-term database 112.

In some non-limiting embodiments, the file of executable code mayinclude instructions that, when executed, cause long-term transactionaggregate data associated with a transaction aggregate for an account ofa user to be retrieved from a long-term database 112. Additionally oralternatively, the file of executable code may include instructionsthat, when executed, cause short-term transaction aggregate dataassociated with a transaction aggregate for an account of a user to beretrieved from a short-term database 110.

In some non-limiting embodiments, code generator system 102 may executethe file of executable code and retrieve transaction aggregate dataassociated with a transaction aggregate for the account of the user fromshort-term database 110 and/or long-term database 112. Additionally oralternatively, orchestration system 106 and/or AI risk determinationsystem 108 may execute the file of executable code and retrievetransaction aggregate data associated with a transaction aggregate forthe account of the user from short-term database 110 and/or long-termdatabase 112.

In some non-limiting embodiments, code generator system 102 may generatethe file of executable code to include instructions that, when executed,cause the transaction aggregate data associated with the transactionaggregate for the account of the user to be retrieved from apredetermined database of a plurality of databases. For example, codegenerator system 102 may generate the file of executable code to includeinstructions that, when executed, cause the transaction aggregate dataassociated with the transaction aggregate for the account of the user tobe retrieved from a predetermined database of short-term database 110and long-term database 112. In some non-limiting embodiments, codegenerator system 102 may determine the predetermined database of theplurality of databases based on data included in the data file receivedfrom user device 104. For example, code generator system 102 maydetermine the predetermined database of the plurality of databases basedon one or more input parameters of a machine learning model and/or oneor more keys associated with one or more parameters of a transactionaggregate for an account of a user. Additionally or alternatively, codegenerator system 102 may determine the predetermined database of theplurality of databases based on an identifier of a machine learningmodel included in the data file.

In some non-limiting embodiments, orchestration system 106 may receive arequest for risk assessment determination of a payment transactioninvolving the account of the user. For example, orchestration system 106may receive the request for risk assessment determination of the paymenttransaction involving the account of the user from user device 104. Insome non-limiting embodiments, the request for risk assessmentdetermination may include an account identifier of the account of theuser. In some non-limiting embodiments, orchestration system 106 maytransmit the request for risk assessment determination based onreceiving the request for risk assessment determination from user device104. For example, orchestration system 106 may transmit the request forrisk assessment determination to code generator system 102 based onreceiving the request for risk assessment determination.

In some non-limiting embodiments, code generator system 102,orchestration system 106, and/or AI risk determination system 108 mayretrieve transaction aggregate data associated with a transactionaggregate for the account of the user from short-term database 110and/or long-term database 112. For example, orchestration system 106 mayretrieve transaction aggregate data associated with a transactionaggregate from short-term database 110 and/or long-term database 112based on the account identifier of the account of the user included inthe request for risk assessment determination. In such an example, codegenerator system 102, orchestration system 106, and/or AI riskdetermination system 108 may determine a user profile that correspondsto the account identifier of the account of the user and the transactionaggregate data associated with the transaction aggregate for the accountof the user may be retrieved from the user profile. In some non-limitingembodiments, code generator system 102, orchestration system 106, and/orAI risk determination system 108 may retrieve short-term aggregationdata associated with the transaction aggregate for the account of theuser from short-term database and/or long-term aggregation dataassociated with the transaction aggregate for the account of the userfrom long-term database 112.

In some non-limiting embodiments, code generator system 102,orchestration system 106, and/or AI risk determination system 108 mayretrieve transaction aggregate data associated with a transactionaggregate for the account of the user from short-term database 110and/or long-term database 112 based on executing the file of executablecode. For example, code generator system 102, orchestration system 106,and/or AI risk determination system 108 may retrieve the transactionaggregate data associated with the transaction aggregate for the accountof the user based on executing the file of executable code at apredetermined time interval. In some non-limiting embodiments, thepredetermined time interval may include a daily time interval, a weeklytime interval, a monthly time interval, and/or the like.

In some non-limiting embodiments, AI risk determination system 108 maydetermine a risk assessment score associated with the paymenttransaction involving the account of the user using the transactionaggregate data associated with the transaction aggregate for the accountof the user. For example, AI risk determination system 108 may determinethe risk assessment score using the transaction aggregate dataassociated with the transaction aggregate for the account of the user asan input to a risk determination machine learning model. In somenon-limiting embodiments, AI risk determination system 108 may transmitthe risk assessment score to orchestration system 106 based ondetermining the risk assessment score. In some non-limiting embodiments,orchestration system 106 may determine whether the risk assessment scoreassociated with the payment transaction involving the account of theuser satisfies a risk assessment threshold. In some non-limitingembodiments, orchestration system 106 may perform an action based ondetermining that the risk assessment score associated with the paymenttransaction involving the account of the user satisfies the riskassessment threshold. In some non-limiting embodiments, orchestrationsystem 106 may forego performing the action or a may perform a differentaction based on determining that the risk assessment score associatedwith the payment transaction involving the account of the user does notsatisfy the risk assessment threshold. In some non-limiting embodiments,orchestration system 106 may transmit a message including an indicationthat the payment transaction is authorized based on determining that therisk assessment score associated with the payment transaction involvingthe account of the user satisfies the risk assessment threshold.Orchestration system 106 may transmit a message including an indicationthat the payment transaction is not authorized based on determining thatthe risk assessment score associated with the payment transactioninvolving the account of the user does not satisfy the risk assessmentthreshold.

Referring now to FIGS. 4A-4E, illustrated is a non-limiting embodimentof an implementation 400 of a process for generating code to retrieveaggregation data for machine learning models. As shown by referencenumber 415 in FIG. 4A, orchestration system 106 may receive an XML datafile associated with a machine learning model. In some non-limitingembodiments, the XML data file includes data associated with one or moreinput parameters of a machine learning model. As shown by referencenumber 420 in FIG. 4A, code generator system 102 may receive the XMLdata file from orchestration system 106 based on orchestration system106 transmitting the XML data file to code generator system 102.

As shown by reference number 425 in FIG. 4B, code generator system 102may generate a code generation template based on the data associatedwith one or more input parameters of the machine learning model includedin the XML data file. In some non-limiting embodiments, the codegeneration template comprises one or more keys associated with one ormore parameters of a transaction aggregate for an account of a user andthe one or more parameters of the transaction aggregate for the accountof the user may be based on one or more parameters of a plurality ofpayment transactions involving the account of the user. As shown byreference number 430 in FIG. 4B, code generator system 102 may generatea file of executable code based on the code generation template. In somenon-limiting embodiments, the file of executable code comprisesinstructions that, when executed by at least one processor, cause the atleast one processor to retrieve transaction aggregate data associatedwith the transaction aggregate for the account of the user.

As shown by reference number 435 in FIG. 4C, orchestration system 106may receive a request for risk assessment determination of a paymenttransaction (e.g., a real-time payment transaction) involving theaccount of the user from user device 104. As shown by reference number440 in FIG. 4D, orchestration system 106 may retrieve transactionaggregate data associated with a transaction aggregate for the accountof the user.

As shown by reference number 445 in FIG. 4E, orchestration system 106may transmit the transaction aggregate data associated with thetransaction aggregate for the account of the user to AI riskdetermination system 108. As shown by reference number 450 in FIG. 4E,AI risk determination system 108 may determine a risk assessment scoreassociated with the payment transaction involving the account of theuser based on the transaction aggregate data associated with thetransaction aggregate. For example, AI risk determination system 108 maydetermine the risk assessment score associated with the paymenttransaction involving the account of the user using the transactionaggregate data associated with the transaction aggregate for the accountof the user as an input to the machine learning model. As shown byreference number 455 in FIG. 4E, orchestration system 106 may receivethe risk assessment score associated with the payment transactioninvolving the account of the user from AI risk determination system 108.As shown by reference number 460 in FIG. 4E, orchestration system 106may perform an action based on the risk assessment score.

Although the above methods, systems, and computer program products havebeen described in detail for the purpose of illustration based on whatis currently considered to be the most practical and preferredembodiments or aspects, it is to be understood that such detail issolely for that purpose and that the present disclosure is not limitedto the described embodiments or aspects but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present disclosure contemplates that, to theextent possible, one or more features of any embodiment or aspect can becombined with one or more features of any other embodiment or aspect.

What is claimed is:
 1. A system, comprising: at least one processorprogrammed or configured to: receive an XML data file, wherein the XMLdata file comprises data associated with one or more input parameters ofa machine learning model; generate a code generation template based onthe data associated with one or more input parameters of the machinelearning model included in the XML file, where the code generationtemplate comprises one or more keys associated with one or moreparameters of a transaction aggregate for an account of a user, whereinthe one or more parameters of the transaction aggregate for the accountof the user are based on one or more parameters of a plurality ofpayment transactions involving the account of the user; and generate afile of executable code based on the code generation template, whereinthe file of executable code comprises instructions that, when executedby at least one processor, cause at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user.
 2. The system of claim 1, wherein the codegeneration template comprises a template for an SQL query and whereinthe template for the SQL query comprises the one or more keys associatedwith one or more parameters of the transaction aggregate for the accountof the user.
 3. The system of claim 1, wherein, when generating the fileof executable code, the at least one processor is programmed orconfigured to: generate the file of executable code that, when executedby at least one processor, causes at least one processor to retrievetransaction aggregate data associated with the transaction aggregate forthe account of the user from a predetermined database of a plurality ofdatabases.
 4. The system of claim 1, wherein the at least one processoris further programmed or configured to: receive a request for riskassessment determination of a payment transaction involving the accountof the user from a client device; retrieve first transaction aggregatedata associated with a first transaction aggregate for the account ofthe user; and determine a risk assessment score associated with thepayment transaction involving the account of the user using the firsttransaction aggregate data associated with the first transactionaggregate for the account of the user as an input to the machinelearning model.
 5. The system of claim 4, wherein, when retrieving firsttransaction aggregate data associated with the first transactionaggregate for the account of the user, the at least one processor isprogrammed or configured to: retrieve short-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a short-term database; and retrieve long-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a long-term database.
 6. The system of claim 1, wherein, whenretrieving the first transaction aggregate data associated with thefirst transaction aggregate for the account of the user, the at leastone processor is programmed or configured to: retrieve the firsttransaction aggregate data associated with the first transactionaggregate for the account of the user from one or more databases basedon executing the file of executable code.
 7. The system of claim 1,wherein, when receiving the XML file, the at least one processor isprogrammed or configured to: receive the XML file from a client devicevia a web application, and wherein the data associated with one or moreinput parameters of the machine learning model is based on data receivedvia the web application.
 8. A computer-implemented method, comprising:receiving, with at least one processor, a data file, wherein the datafile comprises data associated with one or more input parameters of amachine learning model; generating, with at least one processor, a codegeneration template based on the data associated with one or more inputparameters of the machine learning model included in the data file,where the code generation template comprises one or more keys associatedwith one or more parameters of a transaction aggregate for an account ofa user; and generating, with at least one processor, an executable codefile based on the code generation template, wherein the file ofexecutable code comprises instructions that, when executed by at leastone processor, cause the at least one processor to retrieve transactionaggregate data associated with the transaction aggregate for the accountof the user.
 9. The method of claim 8, wherein generating the file ofexecutable code file comprises: generating the file of executable codethat, when executed by at least one processor, causes at least oneprocessor to retrieve transaction aggregate data associated with thetransaction aggregate for the account of the user from a predetermineddatabase of a plurality of databases.
 10. The method of claim 8, furthercomprising: receiving a request for risk assessment determination of apayment transaction involving the account of the user from a clientdevice; retrieving first transaction aggregate data associated with afirst transaction aggregate for the account of the user; and determininga risk assessment score associated with the payment transactioninvolving the account of the user using the first transaction aggregatedata associated with the first transaction aggregate for the account ofthe user as an input to the machine learning model.
 11. The method ofclaim 10, wherein retrieving first transaction aggregate data associatedwith the first transaction aggregate for the account of the usercomprises: retrieving short-term aggregation data associated with thefirst transaction aggregate for the account of the user from ashort-term database; and retrieving long-term aggregation dataassociated with the first transaction aggregate for the account of theuser from a long-term database.
 12. The method of claim 10, whereinretrieving the first transaction aggregate data associated with thefirst transaction aggregate for the account of the user, the at leastone processor is programmed or configured to: retrieve the firsttransaction aggregate data associated with the first transactionaggregate for the account of the user from one or more databases basedon executing the file of executable code.
 13. The method of claim 10,further comprising: determining whether the risk assessment scoreassociated with the payment transaction involving the account of theuser satisfies a risk assessment threshold; and performing an actionbased on determining that the risk assessment score associated with thepayment transaction involving the account of the user satisfies a riskassessment threshold.
 14. The method of claim 8, wherein receiving thedata file comprises: receiving an XML file from a client device via aweb application, and wherein the data associated with one or more inputparameters of the machine learning model is based on data received viathe web application.
 15. A computer program product comprising at leastone non-transitory computer-readable medium comprising one or moreinstructions that, when executed by at least one processor, cause the atleast one processor to: receive a data file associated with a machinelearning model; generate a code generation template based on receivingthe data file associated with the machine learning model, where the codegeneration template comprises one or more keys associated with one ormore parameters of a transaction aggregate for an account of a user,wherein the one or more parameters of the transaction aggregate for theaccount of the user are based on one or more parameters of a pluralityof payment transactions involving the account of the user; and generatea file of executable code based on the code generation template, whereinthe file of executable code comprises instructions that, when executedby the at least one processor; cause the at least one processor toretrieve transaction aggregate data associated with the transactionaggregate for the account of the user.
 16. The computer program productof claim 15, wherein the code generation template comprises a templatefor an SQL query and wherein the template for the SQL query comprisesthe one or more keys associated with one or more parameters of thetransaction aggregate for the account of the user.
 17. The computerprogram product of claim 16, wherein; the one or more instructions thatcause the at least one processor to generate the file of executable codefile, cause the at least one processor to: generate the file ofexecutable code that, when executed by at least one processor, causes atleast one processor to retrieve transaction aggregate data associatedwith the transaction aggregate for the account of the user from apredetermined database of a plurality of databases.
 18. The computerprogram product of claim 17, wherein the one or more instructionsfurther cause the at least one processor to: receive a request for riskassessment determination of a payment transaction involving the accountof the user from a client device; retrieve first transaction aggregatedata associated with a first transaction aggregate for the account ofthe user; and determine a risk assessment score associated with thepayment transaction involving the account of the user using the firsttransaction aggregate data associated with the first transactionaggregate for the account of the user as an input to the machinelearning model.
 19. The computer program product of claim 15, wherein,the one or more instructions that cause the at least one processor toretrieve the first transaction aggregate data associated with the firsttransaction aggregate for the account of the user, cause the at leastone processor to: retrieve short-term aggregation data associated withthe first transaction aggregate for the account of the user from ashort-term database; and retrieve long-term aggregation data associatedwith the first transaction aggregate for the account of the user from along-term database.
 20. The computer program product of claim 15,wherein, the one or more instructions that cause the at least oneprocessor to receive the XML file, cause the at least one processor to:receive the XML file from a client device via a web application, andwherein the data associated with one or more input parameters of themachine learning model is based on data received via the webapplication.